Geometric prompting for controlling cad generation

Abstract

Various embodiments set forth a techniques for generating computer-aided design (CAD) models that includes receiving a plurality of inputs via a user interface, generating a plurality of geometric prompts based on the plurality of inputs, executing a trained machine-learning model on the geometric prompts to generate CAD data, and generating at least one CAD model based on the CAD data. Advantageously, the disclosed techniques can substantially facilitate the overall process of designing CAD objects and CAD models of differing levels of complexity, thereby increasing the accessibility of CAD software and applications to a wider array of users with differing skill sets.

What is claimed is: 
     
         1 . A computer-implemented method for generating computer-aided design (CAD) models, the method comprising:
 generating a plurality of geometric prompts based on a plurality of inputs;   executing a trained machine-learning model on the geometric prompts to generate CAD data; and   generating at least one CAD model based on the CAD data.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein generating a plurality of geometric prompts comprising executing one or more functions on the plurality of inputs to convert the plurality of inputs into the plurality of geometric prompts. 
     
     
         3 . The computer-implemented method of  claim 2 , wherein the plurality of geometric prompts includes a prefix that designates a beginning of the plurality of geometric prompts and a suffix that designates an ending of the plurality of geometric prompts. 
     
     
         4 . The computer-implemented method of  claim 1 , wherein generating at least one CAD model comprises executing one or more drawing functions on the CAD data. 
     
     
         5 . The computer-implemented method of  claim 4 , wherein the CAD data comprises domain specific language commands. 
     
     
         6 . The computer-implemented method of  claim 1 , wherein executing the trained machine-learning model on the geometric prompts to generate the CAD data comprises passing the geometric prompts to an encoder to generate a plurality of tokens, and passing the plurality of tokens to a decoder to generate the CAD data via cross attention. 
     
     
         7 . The computer-implemented method of  claim 1 , further comprising:
 receiving a set of CAD models;   generating associated CAD data for the set of CAD models; and   generating a set of geometric prompts based on the associated CAD data.   
     
     
         8 . The computer-implemented method of  claim 7 , further comprising training an untrained machine-learning model using the associated CAD data and the set of geometric prompts. 
     
     
         9 . The computer-implemented method of  claim 8 , wherein training the untrained machine learning model comprises:
 inputting the set of geometric prompts into the untrained machine learning model to generate intermediate CAD data;   computing one or more losses based on the intermediate CAD data and ground truth CAD data; and   updating the untrained machine-learning model based on the one or more losses.   
     
     
         10 . The computer-implemented of method  claim 7 , wherein generating the set of geometric prompts comprises executing one or more geometric functions on each CAD model included in the set of CAD models to analyze to analyze a geometry associated with the CAD model. 
     
     
         11 . The computer-implemented method of  claim 1 , wherein the plurality of inputs received via the user interface comprises a plurality of control parameters. 
     
     
         12 . The computer-implemented method of  claim 1 , wherein the at least one CAD model comprises a two-dimensional CAD profile or a three-dimensional boundary representation model. 
     
     
         13 . The computer-implemented method of  claim 1 , further comprising receiving the plurality of inputs via a user interface. 
     
     
         14 . The computer-implemented method of  claim 1 , wherein the plurality of inputs is associated with at least one stick model representation of a linkage or at least one assembly interface specification. 
     
     
         15 . One or more non-transitory computer-readable media including instructions that, when executed by one or more processors, cause the one or more processors to perform that steps of:
 receiving a plurality of inputs via a user interface;   generating a plurality of geometric prompts based on the plurality of inputs;   executing a trained machine-learning model on the geometric prompts to generate CAD data; and   generating at least one CAD model based on the CAD data.   
     
     
         16 . The one or more non-transitory computer-readable media of  claim 15 , wherein the plurality of control parameters includes at least one of a center of gravity, a bounding box, or a hole location. 
     
     
         17 . The one or more non-transitory computer-readable media of  claim 15 , wherein a data collector engine generates CAD data and a set of geometric prompts based on one or more stored CAD models, wherein the CAD data and set of geometric prompts are used to train a CAD data generator. 
     
     
         18 . The one or more non-transitory computer-readable media of  claim 15 , wherein a CAD generation application includes a trained CAD data generator that generates CAD data based on geometric prompts, wherein control parameters received by the CAD application program are converted into the geometric prompts. 
     
     
         19 . The one or more non-transitory computer-readable media of  claim 15 , wherein generating a plurality of geometric prompts comprising executing one or more functions on the plurality of inputs to convert the plurality of inputs into the plurality of geometric prompts. 
     
     
         20 . The one or more non-transitory computer-readable media of  claim 19 , wherein the plurality of geometric prompts includes a prefix that designates a beginning of the plurality of geometric prompts and a suffix that designates an ending of the plurality of geometric prompts. 
     
     
         21 . The one or more non-transitory computer-readable media of  claim 15 , wherein the CAD data comprises domain specific language commands, and generating at least one CAD model comprises executing one or more drawing functions on the domain specific language commands. 
     
     
         22 . The one or more non-transitory computer-readable media of  claim 15 , further comprising:
 receiving a set of CAD models;   generating associated CAD data for the set of CAD models; and   generating a set of geometric prompts based on the associated CAD data.   
     
     
         23 . The one or more non-transitory computer-readable media of  claim 22 , further comprising training an untrained machine-learning model using the associated CAD data and the set of geometric prompts. 
     
     
         24 . A computer system, comprising:
 one or more memories that include instructions; and   one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to perform the steps of:
 receiving a plurality of inputs via a user interface; 
 generating a plurality of geometric prompts based on the plurality of inputs; 
 executing a trained machine-learning model on the geometric prompts to generate CAD data; and 
 generating at least one CAD model based on the CAD data.